
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowerCamelCase_    :			List[Any]    =	TypeVar("""T""")

class 							_UpperCamelCase					(       Generic[T]	):






			'''simple docstring'''

			def __init__(							self			:							List[str]    ,						snake_case_			:							list[T]    ,						snake_case_			:							Callable[[T, T], T]   ):
							UpperCamelCase_:				Any | T						=    None

							UpperCamelCase_:				int						=    len(snake_case_   )
							UpperCamelCase_:				list[T]						=    [any_type for _ in range(self.N   )] + arr
							UpperCamelCase_:				str						=    fnc
							self.build()

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							for p in range(self.N - 1    ,						0    ,						-1   ):
											UpperCamelCase_:				Tuple						=    self.fn(self.st[p * 2]    ,						self.st[p * 2 + 1]   )

			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							int    ,						snake_case_			:							T   ):
							p += self.N
							UpperCamelCase_:				str						=    v
							while p > 1:
											UpperCamelCase_:				str						=    p // 2
											UpperCamelCase_:				Dict						=    self.fn(self.st[p * 2]    ,						self.st[p * 2 + 1]   )




			def    lowerCAmelCase__						(							self			:							Any    ,						snake_case_			:							int    ,						snake_case_			:							int   ):  # noqa: E741
							UpperCamelCase_, UpperCamelCase_:				Optional[Any]						=    l + self.N, r + self.N

							UpperCamelCase_:				T | None						=    None
							while l <= r:
											if l % 2 == 1:
															UpperCamelCase_:				Union[str, Any]						=    self.st[l] if res is None else self.fn(snake_case_    ,						self.st[l]   )
											if r % 2 == 0:
															UpperCamelCase_:				List[Any]						=    self.st[r] if res is None else self.fn(snake_case_    ,						self.st[r]   )
											UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    (l + 1) // 2, (r - 1) // 2
							return res


if __name__ == "__main__":
							from functools import reduce

							lowerCamelCase_    :			Optional[Any]    =	[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

							lowerCamelCase_    :			Optional[Any]    =	{
							    0: 7,
							    1: 2,
							    2: 6,
							    3: -14,
							    4: 5,
							    5: 4,
							    6: 7,
							    7: -10,
							    8: 9,
							    9: 10,
							    10: 12,
							    11: 1,
							}

							lowerCamelCase_    :			Dict    =	SegmentTree(test_array, min)
							lowerCamelCase_    :			Optional[Any]    =	SegmentTree(test_array, max)
							lowerCamelCase_    :			Tuple    =	SegmentTree(test_array, lambda a, b: a + b)







							def 							A__       (    )	->						None:
											for i in range(len(lowerCamelCase    )    ):
															for j in range(lowerCamelCase  ,     len(lowerCamelCase    )    ):
																			UpperCamelCase_:				Tuple						=    reduce(lowerCamelCase  ,     test_array[i : j + 1]    )
																			UpperCamelCase_:				List[str]						=    reduce(lowerCamelCase  ,     test_array[i : j + 1]    )
																			UpperCamelCase_:				Tuple						=    reduce(lambda lowerCamelCase  ,     lowerCamelCase    : a + b  ,     test_array[i : j + 1]    )
																			assert min_range == min_segment_tree.query(lowerCamelCase  ,     lowerCamelCase    )
																			assert max_range == max_segment_tree.query(lowerCamelCase  ,     lowerCamelCase    )
																			assert sum_range == sum_segment_tree.query(lowerCamelCase  ,     lowerCamelCase    )

							test_all_segments()

							for index, value in test_updates.items():
														lowerCamelCase_    :			str    =	value
														min_segment_tree.update(index, value)
														max_segment_tree.update(index, value)
														sum_segment_tree.update(index, value)
														test_all_segments()








import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(CMStochasticIterativeScheduler,)
			__UpperCamelCase   :    List[str]          =			10

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_   )
							return config

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Tuple						=    10

							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    self.scheduler_classes[0](**snake_case_   )

							scheduler.set_timesteps(snake_case_   )

							UpperCamelCase_:				int						=    scheduler.timesteps[0]
							UpperCamelCase_:				str						=    scheduler.timesteps[1]

							UpperCamelCase_:				Optional[int]						=    self.dummy_sample
							UpperCamelCase_:				int						=    0.1 * sample

							UpperCamelCase_:				Optional[Any]						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample
							UpperCamelCase_:				Any						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample

							self.assertEqual(output_a.shape    ,						sample.shape   )
							self.assertEqual(output_a.shape    ,						output_a.shape   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				List[str]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Optional[int]						=    1
							scheduler.set_timesteps(snake_case_   )
							UpperCamelCase_:				Any						=    scheduler.timesteps

							UpperCamelCase_:				int						=    torch.manual_seed(0   )

							UpperCamelCase_:				Any						=    self.dummy_model()
							UpperCamelCase_:				Union[str, Any]						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_   ):
											# 1. scale model input
											UpperCamelCase_:				str						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Union[str, Any]						=    pred_prev_sample

							UpperCamelCase_:				int						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				List[Any]						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 192.7614   ) < 1e-2
							assert abs(result_mean.item() - 0.2510   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Dict						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[Any]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				str						=    [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_   )
							UpperCamelCase_:				int						=    scheduler.timesteps

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )

							UpperCamelCase_:				List[Any]						=    self.dummy_model()
							UpperCamelCase_:				int						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:				Optional[int]						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				Dict						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				List[str]						=    pred_prev_sample

							UpperCamelCase_:				Union[str, Any]						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				str						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 347.6357   ) < 1e-2
							assert abs(result_mean.item() - 0.4527   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				int						=    self.scheduler_classes[0]
							UpperCamelCase_:				Optional[int]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Any						=    [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_    ,						msg="""`timesteps` must be in descending order."""   ):
											scheduler.set_timesteps(timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Tuple						=    [39, 30, 12, 1, 0]
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )

							with self.assertRaises(snake_case_    ,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
											scheduler.set_timesteps(num_inference_steps=snake_case_    ,						timesteps=snake_case_   )




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				int						=    self.get_scheduler_config()
							UpperCamelCase_:				int						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				int						=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_    ,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""    ,						):
											scheduler.set_timesteps(timesteps=snake_case_   )






